Overview

Dataset statistics

Number of variables27
Number of observations663
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.0 KiB
Average record size in memory216.2 B

Variable types

Text2
Categorical17
Numeric8

Alerts

age is highly overall correlated with decageHigh correlation
decage is highly overall correlated with ageHigh correlation
height is highly overall correlated with weight and 1 other fieldsHigh correlation
weight is highly overall correlated with height and 1 other fieldsHigh correlation
bmi is highly overall correlated with weightHigh correlation
final_dx_categorized is highly overall correlated with final_dxHigh correlation
sex is highly overall correlated with heightHigh correlation
final_dx is highly overall correlated with final_dx_categorizedHigh correlation
demographics_firstvisit is highly imbalanced (57.2%)Imbalance
hispanic is highly imbalanced (70.4%)Imbalance
health_history1 is highly imbalanced (83.6%)Imbalance
health_history2 is highly imbalanced (72.8%)Imbalance
health_history3 is highly imbalanced (78.3%)Imbalance
health_history4 is highly imbalanced (81.2%)Imbalance
health_history5 is highly imbalanced (92.6%)Imbalance
health_history6 is highly imbalanced (92.6%)Imbalance
health_history7 is highly imbalanced (86.1%)Imbalance
health_history10 is highly imbalanced (94.7%)Imbalance
health_history11 is highly imbalanced (87.8%)Imbalance
health_history12 is highly imbalanced (52.8%)Imbalance
oasis_id has unique valuesUnique
demographics_id has unique valuesUnique
final_dx_categorized has 47 (7.1%) zerosZeros

Reproduction

Analysis started2023-10-16 03:24:11.993819
Analysis finished2023-10-16 03:24:23.057390
Duration11.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oasis_id
Text

UNIQUE 

Distinct663
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:23.322012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters5304
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique663 ?
Unique (%)100.0%

Sample

1st rowOAS42000
2nd rowOAS42001
3rd rowOAS42002
4th rowOAS42003
5th rowOAS42004
ValueCountFrequency (%)
oas42000 1
 
0.2%
oas42090 1
 
0.2%
oas42002 1
 
0.2%
oas42003 1
 
0.2%
oas42004 1
 
0.2%
oas42006 1
 
0.2%
oas42007 1
 
0.2%
oas42009 1
 
0.2%
oas42010 1
 
0.2%
oas42011 1
 
0.2%
Other values (653) 653
98.5%
2023-10-16T08:54:23.722353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 893
16.8%
2 891
16.8%
O 663
12.5%
A 663
12.5%
S 663
12.5%
0 231
 
4.4%
1 228
 
4.3%
5 222
 
4.2%
6 220
 
4.1%
3 220
 
4.1%
Other values (3) 410
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3315
62.5%
Uppercase Letter 1989
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 893
26.9%
2 891
26.9%
0 231
 
7.0%
1 228
 
6.9%
5 222
 
6.7%
6 220
 
6.6%
3 220
 
6.6%
7 156
 
4.7%
9 128
 
3.9%
8 126
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
O 663
33.3%
A 663
33.3%
S 663
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3315
62.5%
Latin 1989
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 893
26.9%
2 891
26.9%
0 231
 
7.0%
1 228
 
6.9%
5 222
 
6.7%
6 220
 
6.6%
3 220
 
6.6%
7 156
 
4.7%
9 128
 
3.9%
8 126
 
3.8%
Latin
ValueCountFrequency (%)
O 663
33.3%
A 663
33.3%
S 663
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 893
16.8%
2 891
16.8%
O 663
12.5%
A 663
12.5%
S 663
12.5%
0 231
 
4.4%
1 228
 
4.3%
5 222
 
4.2%
6 220
 
4.1%
3 220
 
4.1%
Other values (3) 410
7.7%

demographics_id
Text

UNIQUE 

Distinct663
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:23.905778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters17901
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique663 ?
Unique (%)100.0%

Sample

1st rowOAS42000_demographics_d3000
2nd rowOAS42001_demographics_d3000
3rd rowOAS42002_demographics_d3000
4th rowOAS42003_demographics_d3000
5th rowOAS42004_demographics_d3000
ValueCountFrequency (%)
oas42000_demographics_d3000 1
 
0.2%
oas42090_demographics_d3000 1
 
0.2%
oas42002_demographics_d3000 1
 
0.2%
oas42003_demographics_d3000 1
 
0.2%
oas42004_demographics_d3000 1
 
0.2%
oas42006_demographics_d3000 1
 
0.2%
oas42007_demographics_d3000 1
 
0.2%
oas42009_demographics_d3000 1
 
0.2%
oas42010_demographics_d3000 1
 
0.2%
oas42011_demographics_d3000 1
 
0.2%
Other values (653) 653
98.5%
2023-10-16T08:54:24.189466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2220
 
12.4%
_ 1326
 
7.4%
d 1326
 
7.4%
4 893
 
5.0%
2 891
 
5.0%
3 883
 
4.9%
O 663
 
3.7%
A 663
 
3.7%
s 663
 
3.7%
c 663
 
3.7%
Other values (16) 7710
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8619
48.1%
Decimal Number 5967
33.3%
Uppercase Letter 1989
 
11.1%
Connector Punctuation 1326
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1326
15.4%
s 663
7.7%
c 663
7.7%
i 663
7.7%
h 663
7.7%
p 663
7.7%
a 663
7.7%
r 663
7.7%
g 663
7.7%
o 663
7.7%
Other values (2) 1326
15.4%
Decimal Number
ValueCountFrequency (%)
0 2220
37.2%
4 893
15.0%
2 891
14.9%
3 883
 
14.8%
1 228
 
3.8%
5 222
 
3.7%
6 220
 
3.7%
7 156
 
2.6%
9 128
 
2.1%
8 126
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
O 663
33.3%
A 663
33.3%
S 663
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10608
59.3%
Common 7293
40.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1326
 
12.5%
O 663
 
6.2%
A 663
 
6.2%
s 663
 
6.2%
c 663
 
6.2%
i 663
 
6.2%
h 663
 
6.2%
p 663
 
6.2%
a 663
 
6.2%
r 663
 
6.2%
Other values (5) 3315
31.2%
Common
ValueCountFrequency (%)
0 2220
30.4%
_ 1326
18.2%
4 893
12.2%
2 891
12.2%
3 883
 
12.1%
1 228
 
3.1%
5 222
 
3.0%
6 220
 
3.0%
7 156
 
2.1%
9 128
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2220
 
12.4%
_ 1326
 
7.4%
d 1326
 
7.4%
4 893
 
5.0%
2 891
 
5.0%
3 883
 
4.9%
O 663
 
3.7%
A 663
 
3.7%
s 663
 
3.7%
c 663
 
3.7%
Other values (16) 7710
43.1%

demographics_firstvisit
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
605 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 605
91.3%
1 58
 
8.7%

Length

2023-10-16T08:54:24.312675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:24.444102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 605
91.3%
1 58
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 605
91.3%
1 58
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 605
91.3%
1 58
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 605
91.3%
1 58
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 605
91.3%
1 58
 
8.7%

sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2
338 
1
325 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 338
51.0%
1 325
49.0%

Length

2023-10-16T08:54:24.555385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:24.660585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 338
51.0%
1 325
49.0%

Most occurring characters

ValueCountFrequency (%)
2 338
51.0%
1 325
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 338
51.0%
1 325
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 338
51.0%
1 325
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 338
51.0%
1 325
49.0%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.251885
Minimum21
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:24.777062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile57
Q166
median73
Q378
95-th percentile87.9
Maximum94
Range73
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.592839
Coefficient of variation (CV)0.13276939
Kurtosis1.3256599
Mean72.251885
Median Absolute Deviation (MAD)6
Skewness-0.52882829
Sum47903
Variance92.022561
MonotonicityNot monotonic
2023-10-16T08:54:24.899338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 48
 
7.2%
72 35
 
5.3%
73 32
 
4.8%
74 29
 
4.4%
68 29
 
4.4%
80 27
 
4.1%
77 26
 
3.9%
75 24
 
3.6%
67 24
 
3.6%
78 24
 
3.6%
Other values (42) 365
55.1%
ValueCountFrequency (%)
21 1
 
0.2%
37 1
 
0.2%
38 1
 
0.2%
39 1
 
0.2%
43 1
 
0.2%
44 1
 
0.2%
46 2
0.3%
49 3
0.5%
50 2
0.3%
52 1
 
0.2%
ValueCountFrequency (%)
94 2
 
0.3%
93 4
 
0.6%
92 2
 
0.3%
91 3
 
0.5%
90 8
1.2%
89 8
1.2%
88 7
1.1%
87 6
0.9%
86 14
2.1%
85 7
1.1%

edu
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2
254 
4
169 
3
154 
0
59 
1
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row4
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 254
38.3%
4 169
25.5%
3 154
23.2%
0 59
 
8.9%
1 27
 
4.1%

Length

2023-10-16T08:54:25.027613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:25.144170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 254
38.3%
4 169
25.5%
3 154
23.2%
0 59
 
8.9%
1 27
 
4.1%

Most occurring characters

ValueCountFrequency (%)
2 254
38.3%
4 169
25.5%
3 154
23.2%
0 59
 
8.9%
1 27
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 254
38.3%
4 169
25.5%
3 154
23.2%
0 59
 
8.9%
1 27
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 254
38.3%
4 169
25.5%
3 154
23.2%
0 59
 
8.9%
1 27
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 254
38.3%
4 169
25.5%
3 154
23.2%
0 59
 
8.9%
1 27
 
4.1%

race
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7119155
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:25.273029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.0328192
Coefficient of variation (CV)2.9398759
Kurtosis87.415892
Mean1.7119155
Median Absolute Deviation (MAD)0
Skewness9.371092
Sum1135
Variance25.329269
MonotonicityNot monotonic
2023-10-16T08:54:25.445688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 571
86.1%
2 68
 
10.3%
5 13
 
2.0%
50 7
 
1.1%
3 3
 
0.5%
4 1
 
0.2%
ValueCountFrequency (%)
1 571
86.1%
2 68
 
10.3%
3 3
 
0.5%
4 1
 
0.2%
5 13
 
2.0%
50 7
 
1.1%
ValueCountFrequency (%)
50 7
 
1.1%
5 13
 
2.0%
4 1
 
0.2%
3 3
 
0.5%
2 68
 
10.3%
1 571
86.1%

hispanic
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
606 
9
 
50
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 606
91.4%
9 50
 
7.5%
1 7
 
1.1%

Length

2023-10-16T08:54:25.579475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:25.727097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 606
91.4%
9 50
 
7.5%
1 7
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 606
91.4%
9 50
 
7.5%
1 7
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 606
91.4%
9 50
 
7.5%
1 7
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 606
91.4%
9 50
 
7.5%
1 7
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 606
91.4%
9 50
 
7.5%
1 7
 
1.1%

marriage
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
459 
3
99 
4
56 
2
 
43
5
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 459
69.2%
3 99
 
14.9%
4 56
 
8.4%
2 43
 
6.5%
5 6
 
0.9%

Length

2023-10-16T08:54:25.820386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:25.958921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 459
69.2%
3 99
 
14.9%
4 56
 
8.4%
2 43
 
6.5%
5 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 459
69.2%
3 99
 
14.9%
4 56
 
8.4%
2 43
 
6.5%
5 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 459
69.2%
3 99
 
14.9%
4 56
 
8.4%
2 43
 
6.5%
5 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 459
69.2%
3 99
 
14.9%
4 56
 
8.4%
2 43
 
6.5%
5 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 459
69.2%
3 99
 
14.9%
4 56
 
8.4%
2 43
 
6.5%
5 6
 
0.9%

declong
Real number (ℝ)

Distinct30
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3164103
Minimum0.25
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:26.071492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.66
Q12
median3
Q34
95-th percentile9
Maximum25
Range24.75
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8641114
Coefficient of variation (CV)0.86361795
Kurtosis11.972273
Mean3.3164103
Median Absolute Deviation (MAD)1
Skewness2.7868452
Sum2198.78
Variance8.2031342
MonotonicityNot monotonic
2023-10-16T08:54:26.190927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2 156
23.5%
3 155
23.4%
1 101
15.2%
5 50
 
7.5%
4 48
 
7.2%
6 27
 
4.1%
0.5 20
 
3.0%
7 18
 
2.7%
10 13
 
2.0%
8 13
 
2.0%
Other values (20) 62
 
9.4%
ValueCountFrequency (%)
0.25 8
 
1.2%
0.33 3
 
0.5%
0.5 20
 
3.0%
0.58 1
 
0.2%
0.66 5
 
0.8%
0.75 4
 
0.6%
0.83 2
 
0.3%
1 101
15.2%
1.25 1
 
0.2%
1.5 8
 
1.2%
ValueCountFrequency (%)
25 1
 
0.2%
21 1
 
0.2%
20 2
 
0.3%
16 1
 
0.2%
15 2
 
0.3%
14 3
 
0.5%
13 2
 
0.3%
12 3
 
0.5%
10 13
2.0%
9 7
1.1%

decage
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.923077
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:26.322205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile53
Q163
median70
Q375
95-th percentile84.9
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.18641
Coefficient of variation (CV)0.1477939
Kurtosis1.7416967
Mean68.923077
Median Absolute Deviation (MAD)6
Skewness-0.66753404
Sum45696
Variance103.76296
MonotonicityNot monotonic
2023-10-16T08:54:26.439329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 34
 
5.1%
72 32
 
4.8%
69 31
 
4.7%
73 30
 
4.5%
75 28
 
4.2%
59 28
 
4.2%
71 27
 
4.1%
67 27
 
4.1%
65 27
 
4.1%
66 25
 
3.8%
Other values (46) 374
56.4%
ValueCountFrequency (%)
18 1
0.2%
19 1
0.2%
30 1
0.2%
35 2
0.3%
40 2
0.3%
41 2
0.3%
42 1
0.2%
43 1
0.2%
45 1
0.2%
46 1
0.2%
ValueCountFrequency (%)
92 3
 
0.5%
91 1
 
0.2%
90 1
 
0.2%
89 4
 
0.6%
88 3
 
0.5%
87 9
1.4%
86 8
1.2%
85 5
0.8%
84 9
1.4%
83 12
1.8%

smoke
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
367 
1
257 
2
37 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 367
55.4%
1 257
38.8%
2 37
 
5.6%
3 2
 
0.3%

Length

2023-10-16T08:54:26.572487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:26.667523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 367
55.4%
1 257
38.8%
2 37
 
5.6%
3 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 367
55.4%
1 257
38.8%
2 37
 
5.6%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 367
55.4%
1 257
38.8%
2 37
 
5.6%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 367
55.4%
1 257
38.8%
2 37
 
5.6%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 367
55.4%
1 257
38.8%
2 37
 
5.6%
3 2
 
0.3%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)7.3%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean66.26056
Minimum54.5
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:26.786195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum54.5
5-th percentile60
Q163
median66
Q369.13
95-th percentile73
Maximum77
Range22.5
Interquartile range (IQR)6.13

Descriptive statistics

Standard deviation4.2268327
Coefficient of variation (CV)0.06379108
Kurtosis-0.52892163
Mean66.26056
Median Absolute Deviation (MAD)3
Skewness0.09795148
Sum43798.23
Variance17.866115
MonotonicityNot monotonic
2023-10-16T08:54:26.907967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
64 55
 
8.3%
65 52
 
7.8%
66 49
 
7.4%
67 47
 
7.1%
70 44
 
6.6%
68 43
 
6.5%
69 40
 
6.0%
63 40
 
6.0%
62 39
 
5.9%
72 34
 
5.1%
Other values (38) 218
32.9%
ValueCountFrequency (%)
54.5 1
 
0.2%
55 1
 
0.2%
56 2
 
0.3%
57 2
 
0.3%
57.5 1
 
0.2%
58 7
 
1.1%
58.5 1
 
0.2%
59 9
 
1.4%
59.5 2
 
0.3%
60 27
4.1%
ValueCountFrequency (%)
77 2
 
0.3%
76 7
 
1.1%
75 6
 
0.9%
74.5 1
 
0.2%
74 14
2.1%
73.6 1
 
0.2%
73 16
2.4%
72 34
5.1%
71.5 2
 
0.3%
71 29
4.4%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct282
Distinct (%)42.7%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean170.61952
Minimum86
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:27.028890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile110
Q1140.25
median166
Q3197
95-th percentile244
Maximum450
Range364
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation42.656096
Coefficient of variation (CV)0.25000713
Kurtosis2.7322059
Mean170.61952
Median Absolute Deviation (MAD)28
Skewness0.94352507
Sum112779.5
Variance1819.5425
MonotonicityNot monotonic
2023-10-16T08:54:27.154628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 9
 
1.4%
144 9
 
1.4%
182 9
 
1.4%
132 8
 
1.2%
155 8
 
1.2%
170 8
 
1.2%
180 8
 
1.2%
153 7
 
1.1%
169 7
 
1.1%
123 7
 
1.1%
Other values (272) 581
87.6%
ValueCountFrequency (%)
86 1
 
0.2%
88.5 1
 
0.2%
90 1
 
0.2%
91 1
 
0.2%
95 1
 
0.2%
98 2
0.3%
98.5 1
 
0.2%
98.6 1
 
0.2%
100 3
0.5%
101 4
0.6%
ValueCountFrequency (%)
450 1
0.2%
316 1
0.2%
313 1
0.2%
307 1
0.2%
306 1
0.2%
305 1
0.2%
303 1
0.2%
300 1
0.2%
280 1
0.2%
277 1
0.2%

bmi
Real number (ℝ)

HIGH CORRELATION 

Distinct209
Distinct (%)31.7%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean27.125606
Minimum14.7
Maximum59.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:27.273325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.7
5-th percentile19.595
Q123.4
median26.3
Q329.9
95-th percentile37.305
Maximum59.4
Range44.7
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation5.4333787
Coefficient of variation (CV)0.20030442
Kurtosis2.5122143
Mean27.125606
Median Absolute Deviation (MAD)3.3
Skewness1.0233802
Sum17902.9
Variance29.521604
MonotonicityNot monotonic
2023-10-16T08:54:27.660342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.2 12
 
1.8%
25.8 11
 
1.7%
26.4 10
 
1.5%
26.3 9
 
1.4%
24 9
 
1.4%
28.7 8
 
1.2%
25.1 8
 
1.2%
31.6 7
 
1.1%
25.6 7
 
1.1%
26.6 7
 
1.1%
Other values (199) 572
86.3%
ValueCountFrequency (%)
14.7 1
0.2%
16.5 1
0.2%
17.4 1
0.2%
17.5 2
0.3%
17.6 1
0.2%
17.8 2
0.3%
17.9 2
0.3%
18 1
0.2%
18.3 2
0.3%
18.4 2
0.3%
ValueCountFrequency (%)
59.4 1
0.2%
51.2 1
0.2%
49.1 1
0.2%
46.2 1
0.2%
45.1 1
0.2%
42.8 1
0.2%
41.6 1
0.2%
41.3 1
0.2%
40.8 1
0.2%
40.3 1
0.2%

health_history1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
647 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 647
97.6%
1 16
 
2.4%

Length

2023-10-16T08:54:27.777461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:27.872399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 647
97.6%
1 16
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 647
97.6%
1 16
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 647
97.6%
1 16
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 647
97.6%
1 16
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 647
97.6%
1 16
 
2.4%

health_history2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
632 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 632
95.3%
1 31
 
4.7%

Length

2023-10-16T08:54:27.960645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:28.060390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 632
95.3%
1 31
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 632
95.3%
1 31
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 632
95.3%
1 31
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 632
95.3%
1 31
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 632
95.3%
1 31
 
4.7%

health_history3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
640 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 640
96.5%
1 23
 
3.5%

Length

2023-10-16T08:54:28.138679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:28.253254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 640
96.5%
1 23
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 640
96.5%
1 23
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 640
96.5%
1 23
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 640
96.5%
1 23
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 640
96.5%
1 23
 
3.5%

health_history4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
644 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 644
97.1%
1 19
 
2.9%

Length

2023-10-16T08:54:28.345453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:28.445053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 644
97.1%
1 19
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 644
97.1%
1 19
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 644
97.1%
1 19
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 644
97.1%
1 19
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 644
97.1%
1 19
 
2.9%

health_history5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
657 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Length

2023-10-16T08:54:28.541104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:28.641926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

health_history6
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
657 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Length

2023-10-16T08:54:28.738777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:28.822178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 657
99.1%
1 6
 
0.9%

health_history7
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
650 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 650
98.0%
1 13
 
2.0%

Length

2023-10-16T08:54:28.909959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:29.024521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 650
98.0%
1 13
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 650
98.0%
1 13
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 650
98.0%
1 13
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 650
98.0%
1 13
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 650
98.0%
1 13
 
2.0%

health_history10
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
659 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 659
99.4%
1 4
 
0.6%

Length

2023-10-16T08:54:29.106225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:29.208091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 659
99.4%
1 4
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 659
99.4%
1 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 659
99.4%
1 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 659
99.4%
1 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 659
99.4%
1 4
 
0.6%

health_history11
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
652 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 652
98.3%
1 11
 
1.7%

Length

2023-10-16T08:54:29.301966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:29.387491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 652
98.3%
1 11
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 652
98.3%
1 11
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 652
98.3%
1 11
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 652
98.3%
1 11
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 652
98.3%
1 11
 
1.7%

health_history12
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
596 
1
67 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 596
89.9%
1 67
 
10.1%

Length

2023-10-16T08:54:29.480501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:54:29.577480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 596
89.9%
1 67
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 596
89.9%
1 67
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 596
89.9%
1 67
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 596
89.9%
1 67
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 596
89.9%
1 67
 
10.1%

final_dx
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Alzheimer Disease Dementia
219 
Uncertain - AD possible
121 
MCI
49 
Cognitively Normal
47 
AD/Vascular
40 
Other values (11)
187 

Length

Max length40
Median length39
Mean length20.624434
Min length3

Characters and Unicode

Total characters13674
Distinct characters41
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUncertain - AD possible
2nd rowUncertain - AD possible
3rd rowFTD
4th rowAlzheimer Disease Dementia
5th rowAlzheimer Disease Dementia

Common Values

ValueCountFrequency (%)
Alzheimer Disease Dementia 219
33.0%
Uncertain - AD possible 121
18.3%
MCI 49
 
7.4%
Cognitively Normal 47
 
7.1%
AD/Vascular 40
 
6.0%
Mood/polypharmacy/sleep 31
 
4.7%
Early Onset AD 27
 
4.1%
FTD 25
 
3.8%
Non-Neurodegenerative Neurologic Disease 22
 
3.3%
Vascular Cognitive Impairment (VCI) 16
 
2.4%
Other values (6) 66
 
10.0%

Length

2023-10-16T08:54:29.677946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disease 241
13.8%
alzheimer 219
12.6%
dementia 219
12.6%
ad 160
9.2%
136
 
7.8%
uncertain 121
 
6.9%
possible 121
 
6.9%
mci 49
 
2.8%
cognitively 47
 
2.7%
normal 47
 
2.7%
Other values (20) 382
21.9%

Most occurring characters

ValueCountFrequency (%)
e 2072
15.2%
i 1166
 
8.5%
1079
 
7.9%
a 927
 
6.8%
s 879
 
6.4%
D 728
 
5.3%
r 685
 
5.0%
n 680
 
5.0%
l 631
 
4.6%
m 548
 
4.0%
Other values (31) 4279
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10277
75.2%
Uppercase Letter 2005
 
14.7%
Space Separator 1079
 
7.9%
Dash Punctuation 169
 
1.2%
Other Punctuation 102
 
0.7%
Open Punctuation 16
 
0.1%
Close Punctuation 16
 
0.1%
Math Symbol 10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2072
20.2%
i 1166
11.3%
a 927
9.0%
s 879
8.6%
r 685
 
6.7%
n 680
 
6.6%
l 631
 
6.1%
m 548
 
5.3%
t 527
 
5.1%
o 480
 
4.7%
Other values (10) 1682
16.4%
Uppercase Letter
ValueCountFrequency (%)
D 728
36.3%
A 447
22.3%
N 155
 
7.7%
C 128
 
6.4%
U 121
 
6.0%
M 95
 
4.7%
V 84
 
4.2%
I 81
 
4.0%
O 53
 
2.6%
E 27
 
1.3%
Other values (5) 86
 
4.3%
Space Separator
ValueCountFrequency (%)
1079
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12282
89.8%
Common 1392
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2072
16.9%
i 1166
 
9.5%
a 927
 
7.5%
s 879
 
7.2%
D 728
 
5.9%
r 685
 
5.6%
n 680
 
5.5%
l 631
 
5.1%
m 548
 
4.5%
t 527
 
4.3%
Other values (25) 3439
28.0%
Common
ValueCountFrequency (%)
1079
77.5%
- 169
 
12.1%
/ 102
 
7.3%
( 16
 
1.1%
) 16
 
1.1%
+ 10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2072
15.2%
i 1166
 
8.5%
1079
 
7.9%
a 927
 
6.8%
s 879
 
6.4%
D 728
 
5.3%
r 685
 
5.0%
n 680
 
5.0%
l 631
 
4.6%
m 548
 
4.0%
Other values (31) 4279
31.3%

final_dx_categorized
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5641026
Minimum0
Maximum14
Zeros47
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-10-16T08:54:29.776831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile12
Maximum14
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.9975494
Coefficient of variation (CV)0.87586757
Kurtosis-0.78374937
Mean4.5641026
Median Absolute Deviation (MAD)2
Skewness0.64403094
Sum3026
Variance15.980401
MonotonicityNot monotonic
2023-10-16T08:54:29.875484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 219
33.0%
8 121
18.3%
2 49
 
7.4%
0 47
 
7.1%
7 40
 
6.0%
5 33
 
5.0%
12 31
 
4.7%
4 27
 
4.1%
3 25
 
3.8%
6 16
 
2.4%
Other values (5) 55
 
8.3%
ValueCountFrequency (%)
0 47
 
7.1%
1 219
33.0%
2 49
 
7.4%
3 25
 
3.8%
4 27
 
4.1%
5 33
 
5.0%
6 16
 
2.4%
7 40
 
6.0%
8 121
18.3%
9 7
 
1.1%
ValueCountFrequency (%)
14 15
 
2.3%
13 10
 
1.5%
12 31
 
4.7%
11 12
 
1.8%
10 11
 
1.7%
9 7
 
1.1%
8 121
18.3%
7 40
 
6.0%
6 16
 
2.4%
5 33
 
5.0%

Interactions

2023-10-16T08:54:21.320913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.026936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.131172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.060517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.071555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.209050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.138479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.342031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.460406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.163515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.252530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.174844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.249068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.305477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.504492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.457603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.590580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.297117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.375180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.338065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.414216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.421333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.605737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.568782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.688402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.404675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.494262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.459115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.569098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.555154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.706911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.658970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.804667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.546090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.626905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.580036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.707564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.680090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.838837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.811458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.920990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.698312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.725409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.693419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.829045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.771802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.960723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.941684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:22.055187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.844145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.843356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.831690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:17.960545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.906009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.087812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.072645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:22.160357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:14.973297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:15.937775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:16.943313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:18.086696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:19.028170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:20.206052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T08:54:21.171248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-16T08:54:29.993485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ageracedeclongdecageheightweightbmifinal_dx_categorizeddemographics_firstvisitsexeduhispanicmarriagesmokehealth_history1health_history2health_history3health_history4health_history5health_history6health_history7health_history10health_history11health_history12final_dx
age1.000-0.045-0.0470.960-0.188-0.275-0.212-0.2160.0000.0000.0770.0000.1750.1240.0930.0830.0000.0680.0000.0000.2620.0000.0000.0000.210
race-0.0451.000-0.029-0.047-0.117-0.088-0.0270.0440.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.000
declong-0.047-0.0291.000-0.2700.055-0.000-0.030-0.0420.3000.0670.0000.0000.0000.1410.1750.0000.0000.2040.0000.0000.0000.0000.0520.0000.059
decage0.960-0.047-0.2701.000-0.196-0.266-0.195-0.1950.0000.0190.0440.0000.1720.1250.1240.0850.0000.0260.0000.0000.1580.0000.0000.0000.191
height-0.188-0.1170.055-0.1961.0000.6250.146-0.0140.0340.7670.0940.0000.1400.0130.0630.0000.0000.0120.0000.0000.0240.0000.0000.0430.000
weight-0.275-0.088-0.000-0.2660.6251.0000.8430.0940.0000.4800.0000.0000.0770.0000.0000.0570.1820.0620.1540.1220.0000.0000.0000.0000.100
bmi-0.212-0.027-0.030-0.1950.1460.8431.0000.1320.0000.1800.0000.0000.0000.0000.0000.0000.0000.0800.2040.3990.0000.0630.0000.0580.075
final_dx_categorized-0.2160.044-0.042-0.195-0.0140.0940.1321.0000.0000.0970.0800.0550.0640.0000.0960.0540.0420.1200.0810.0000.0420.0900.0150.1830.995
demographics_firstvisit0.0000.0000.3000.0000.0340.0000.0000.0001.0000.0890.0860.0530.0400.0540.0000.0000.0000.0000.0390.0000.0000.0000.0510.0000.075
sex0.0000.0000.0670.0190.7670.4800.1800.0970.0891.0000.1780.0540.2850.1150.0610.0650.0770.1050.0310.0240.0000.0000.0000.0620.154
edu0.0770.0430.0000.0440.0940.0000.0000.0800.0860.1781.0000.0840.0400.1040.0720.0000.0000.0000.0000.0000.0100.0530.0780.0910.129
hispanic0.0000.0000.0000.0000.0000.0000.0000.0550.0530.0540.0841.0000.0740.0000.0000.0200.0000.0000.0760.0000.0000.0000.0000.0000.000
marriage0.1750.0000.0000.1720.1400.0770.0000.0640.0400.2850.0400.0741.0000.0000.0220.0000.0700.0530.0000.0000.0000.0000.0000.0160.092
smoke0.1240.0000.1410.1250.0130.0000.0000.0000.0540.1150.1040.0000.0001.0000.0000.0400.0000.0600.1000.0000.0000.0000.2010.0000.000
health_history10.0930.0000.1750.1240.0630.0000.0000.0960.0000.0610.0720.0000.0220.0001.0000.0000.0970.1750.0000.0000.2230.0000.0000.0000.076
health_history20.0830.0000.0000.0850.0000.0570.0000.0540.0000.0650.0000.0200.0000.0400.0001.0000.0000.0000.0000.0830.0250.0000.0000.0000.000
health_history30.0000.0000.0000.0000.0000.1820.0000.0420.0000.0770.0000.0000.0700.0000.0970.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
health_history40.0680.0000.2040.0260.0120.0620.0800.1200.0000.1050.0000.0000.0530.0600.1750.0000.0001.0000.0000.0000.1330.0000.0000.0000.138
health_history50.0000.0000.0000.0000.0000.1540.2040.0810.0390.0310.0000.0760.0000.1000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.069
health_history60.0000.0000.0000.0000.0000.1220.3990.0000.0000.0240.0000.0000.0000.0000.0000.0830.0000.0000.0001.0000.0000.0000.0000.0000.000
health_history70.2620.0000.0000.1580.0240.0000.0000.0420.0000.0000.0100.0000.0000.0000.2230.0250.0000.1330.0000.0001.0000.0000.0000.0000.125
health_history100.0000.0000.0000.0000.0000.0000.0630.0900.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1960.082
health_history110.0000.0210.0520.0000.0000.0000.0000.0150.0510.0000.0780.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.161
health_history120.0000.0000.0000.0000.0430.0000.0580.1830.0000.0620.0910.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.1960.0001.0000.256
final_dx0.2100.0000.0590.1910.0000.1000.0750.9950.0750.1540.1290.0000.0920.0000.0760.0000.0000.1380.0690.0000.1250.0820.1610.2561.000

Missing values

2023-10-16T08:54:22.377974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-16T08:54:22.764389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-16T08:54:22.972349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

oasis_iddemographics_iddemographics_firstvisitsexageeduracehispanicmarriagedeclongdecagesmokeheightweightbmihealth_history1health_history2health_history3health_history4health_history5health_history6health_history7health_history10health_history11health_history12final_dxfinal_dx_categorized
0OAS42000OAS42000_demographics_d3000027601921.075160.0162.00031.60000000000Uncertain - AD possible8
1OAS42001OAS42001_demographics_d3000018241013.079169.0160.20023.70000000000Uncertain - AD possible8
2OAS42002OAS42002_demographics_d3000117341013.070169.0208.00030.70000000000FTD3
3OAS42003OAS42003_demographics_d3000028021032.078063.0148.00026.20000000000Alzheimer Disease Dementia1
4OAS42004OAS42004_demographics_d3000027721015.072065.0134.00022.30000000000Alzheimer Disease Dementia1
5OAS42006OAS42006_demographics_d3000027321015.068066.0132.12521.30000000000Alzheimer Disease Dementia1
6OAS42007OAS42007_demographics_d3000117401013.071067.0137.00021.50000000001MCI2
7OAS42009OAS42009_demographics_d3000028421012.082062.0102.00018.70000000000DLB10
8OAS42010OAS42010_demographics_d3000127121037.064160.0130.00025.40000000001Alzheimer Disease Dementia1
9OAS42011OAS42011_demographics_d30000267210114.053063.0214.00037.90000000001Uncertain - AD possible8
oasis_iddemographics_iddemographics_firstvisitsexageeduracehispanicmarriagedeclongdecagesmokeheightweightbmihealth_history1health_history2health_history3health_history4health_history5health_history6health_history7health_history10health_history11health_history12final_dxfinal_dx_categorized
653OAS42716OAS42716_demographics_d3000024341012.041064.5174.629.50000000000Mood/polypharmacy/sleep12
654OAS42717OAS42717_demographics_d3000027601018.068163.0132.023.40001000000Other Non-AD Neurodegenerative Disorder5
655OAS42718OAS42718_demographics_d3000028341015.078062.5121.021.80100010000Alzheimer Disease Dementia1
656OAS42719OAS42719_demographics_d3000027721013.074163.0225.039.90000000000FTD3
657OAS42720OAS42720_demographics_d3000027522011.074064.0169.029.00001000000MCI2
658OAS42721OAS42721_demographics_d3000027342023.070066.0153.024.70000000000AD+Non Neurodegenerative13
659OAS42724OAS42724_demographics_d3000026941013.066166.0130.021.00000000000AD Variant11
660OAS42725OAS42725_demographics_d3000018631013.083165.0172.028.60000000000MCI2
661OAS42726OAS42726_demographics_d3000026031042.058268.0237.036.00000001000Mood/polypharmacy/sleep12
662OAS42727OAS42727_demographics_d3000016841013.065165.5202.033.10000000000MCI2